| """
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| Threshold Network for 3-input XNOR Gate
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|
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| XNOR3(a,b,c) = 1 when even number of inputs are 1 (0 or 2)
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| Built as: XOR(XNOR(a,b), c)
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| """
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|
|
| import torch
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| from safetensors.torch import load_file
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|
|
|
|
| class ThresholdXNOR3:
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| def __init__(self, weights_dict):
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| self.w = weights_dict
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|
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| def __call__(self, a, b, c):
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|
|
| inp1 = torch.tensor([float(a), float(b)])
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| n1 = int((inp1 * self.w['xnor1.layer1.n1.weight']).sum() + self.w['xnor1.layer1.n1.bias'] >= 0)
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| n2 = int((inp1 * self.w['xnor1.layer1.n2.weight']).sum() + self.w['xnor1.layer1.n2.bias'] >= 0)
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|
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| h1 = torch.tensor([float(n1), float(n2)])
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| xnor_ab = int((h1 * self.w['xnor1.layer2.weight']).sum() + self.w['xnor1.layer2.bias'] >= 0)
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|
|
|
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| inp2 = torch.tensor([float(xnor_ab), float(c)])
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| n3 = int((inp2 * self.w['xor2.layer1.n1.weight']).sum() + self.w['xor2.layer1.n1.bias'] >= 0)
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| n4 = int((inp2 * self.w['xor2.layer1.n2.weight']).sum() + self.w['xor2.layer1.n2.bias'] >= 0)
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|
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| h2 = torch.tensor([float(n3), float(n4)])
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| out = int((h2 * self.w['xor2.layer2.weight']).sum() + self.w['xor2.layer2.bias'] >= 0)
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|
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| return float(out)
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|
|
| @classmethod
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| def from_safetensors(cls, path="model.safetensors"):
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| return cls(load_file(path))
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|
|
|
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| if __name__ == "__main__":
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| weights = load_file("model.safetensors")
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| model = ThresholdXNOR3(weights)
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|
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| print("3-input XNOR Gate Truth Table:")
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| print("-" * 30)
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| correct = 0
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| for a in [0, 1]:
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| for b in [0, 1]:
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| for c in [0, 1]:
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| out = int(model(a, b, c))
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|
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| expected = 1 - (a ^ b ^ c)
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| status = "OK" if out == expected else "FAIL"
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| if out == expected:
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| correct += 1
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| print(f"XNOR3({a}, {b}, {c}) = {out} [{status}]")
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| print(f"\nTotal: {correct}/8 correct")
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|
|
